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In Acta obstetricia et gynecologica Scandinavica ; h5-index 38.0

INTRODUCTION : There is currently no satisfactory model for predicting malignant transformation of endometriosis. The aim of this study was to construct and evaluate a risk model incorporating noninvasive clinical parameters to predict endometriosis-associated ovarian cancer (EAOC) in patients with endometriosis.

MATERIAL AND METHODS : We enrolled 6809 patients with endometriosis confirmed by pathology, and randomly allocated them to training (n = 4766) and testing cohorts (n = 2043). The proportion of patients with EAOC in each cohort was similar. We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method - gradient-boosting decision tree - to construct a predictive model for EAOC and to evaluate the accuracy of the model. We also constructed a multivariate logistic regression model inclusive of the EAOC-associated risk factors using a back stepwise procedure. Then we compared the performance of the two risk-predicting models using DeLong's test.

RESULTS : The occurrence of EAOC was 1.84% in this study. The logistic regression model comprised 10 selected features and demonstrated good discrimination in the testing cohort, with an area under the curve (AUC) of 0.891 (95% confidence interval [CI] 0.821-0.960), sensitivity of 88.9%, and specificity of 76.7%. The risk model based on machine learning had an AUC of 0.942 (95% CI 0.914-0.969), sensitivity of 86.8%, and specificity of 86.7%. The machine learning-based risk model performed better than the logistic regression model in DeLong's test (p = 0.036). Furthermore, in a prospective dataset, the machine learning-based risk model had an AUC of 0.8758, a sensitivity of 94.4%, and a specificity of 73.8%.

CONCLUSIONS : The machine learning-based risk model was constructed to predict EAOC and had high sensitivity and specificity. This model could be of considerable use in helping reduce medical costs and designing follow-up schedules.

Chao Xiaopei, Wang Shu, Lang Jinghe, Leng Jinhua, Fan Qingbo


endometriosis, machine learning, malignant transformation, ovarian cancer, risk model